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Getting the most out of any GenAI app depends on prompting the app effectively.

This module introduces learners to the basic skills necessary for effective prompt engineering, or for crafting one’s prompts so that the information elicited from the app in question is as helpful as possible given one’s goals. While many discoveries stem from play—from messing around to see what works—having a fully formulated idea of what one wishes to accomplish shortens the prompt-crafting phase, sometimes drastically.

To save time, we recommend (after goofing around at least a little bit to get to know the parameters of a given app) stepping back to work on problem formulation. A well-formulated problem gives a person a real head start when optimizing GenAI.

As Oguz Acar explains, problem formulation, in which we identify and analyze problems to delineate the scope, focus, and boundaries of a goal, depends on "a comprehensive understanding of the problem domain and ability to distill real-world issues" into concrete, delimited terms. For example, we may need to serve three equally hungry people with a 24-inch pizza.

Prompt engineering, in contrast, “focuses on crafting the optimal textual input by selecting the appropriate words, phrases, sentence structures, and punctuation” (Acar, 2023). So, for instance, with the pizza problem being formulated, we might prompt a GenAI tool to tell us where to cut into the pizza, which is 24 inches in diameter (and round), to divide the pie equally. We might ask for reference points that track with a 12-hour analog clock. This will yield much more practical output than asking, for instance, how many square inches of pizza to give each person.